CONTEMPORARY PERSPECTIVES ON AYURVEDA & CHANGING PARADIGMS

Medical science is advancing by leaps and bounds, exploring the intricacies and unravelling the mysteries of human life. Ever since the dawn of his turbulent history, man has evolved several ways of coping with illness. Every country has developed a medical system presenting a unique configuration designed to be compatible with its future, meeting the needs of its population. Thus the ‘traditional medicine’, which is full of experiences, astute observations and fancy formulae reflecting a combination of inspiration, intuition, information, facts and results, has incarnated.Ayurveda, literally meaning “the science of lifespan”, is the traditional medicine system of India. Its natural healing modality has been in existence for about 5000 years. Ayurveda is widely acknowledged to be the world’s oldest system of healthcare. WHO regards it as “the world’s most ancient, scientific, holistic, complete, natural system of healthcare and is the forerunner of all other great systems practiced today.

SCIENTIFIC VALIDATION OF AYURVEDIC CONCEPT OF PRAKRITI (PSYCHO-SOMATIC CONSTITUTION) - CURRENT EVIDENCES

Ayurveda is an ancient Indian healing system with personalized approach documented and practiced since ages. Ayurveda is not merely a system of medicine, in a broader sense it is the “Science of Holistic Living and Art of Natural Healing”. Ayurveda has a unique way of classifying human population based on individual constitution or Prakriti. Ayurveda's Tridosha theory identifies principles of movement (Vata), metabolism (Pitta), and structure (Kapha) as discrete phenotypic groupings. As per this system, every individual is born with his or her own basic constitution, which to a great extent regulates inter-individual variability in susceptibility to diseases and response to external environment, diet and drugs. In the realm of modern predictive medicine, efforts are being directed towards capturing disease phenotypes with greater precision for successful identification of markers for prospective disease conditions. Due to contemporary technological advancements, newer approaches are emerging in different sciences which are beyond their frontiers, of which Precision medicine is newer one. It is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. It seems to be the continuation or advancement of personalized predictive medicine. In this context different study discussed in the article provides the identification of a genomic link to the theory of Prakriti led to a search for possible classification of people on their Prakriti based on their genetic makeup. These studies could eventually lead to a personalization of medical practice on the basis of Prakriti as is conceived in Ayurveda. This reappraisal of Ayurveda in light of fundamental science and its advances would be immensely helpful to perceive Ayurveda in true scientific fervor

A novel scheduling algorithm to maximize the D2D spatial reuse in LTE networks

In order to offload base station (BS) traffic and to enhance efficiency of spectrum, operators can activate many Device-to-Device (D2D) pairs or links in LTE networks. This increases the overall spectral efficiency because the same Resource Blocks (RBs) are used across cellular UEs (CUEs) (i.e., all UEs connected to BS for both C-Plane and D-plane communication) and D2D links (i.e., where the UEs are connected to BS only for C-plane communication). However, significant interference problems can be caused by D2D communications as the same RBs are being shared. In our work, we address this problem by proposing a novel scheduling algorithm, Efficient Scheduling and Power control Algorithm for D2Ds (ESPAD), which reuses the same RBs and tries to maximize the overall network throughput without affecting the CUEs throughput. ESPAD algorithm also ensures that Signal to Noise plus Interference Ratio (SINR) for each of the D2D links is maintained above a certain predefined threshold. The aforementioned properties of ESPAD algorithm makes sure that the CUEs do not experience very high interference from the D2Ds. It is observed that even when the SINRdrop (i.e., maximum permissible drop in SINR of CUEs) is as high as 10 dB, there is no drastic decrease in CUEs throughput (only 3.78%). We also compare our algorithm against other algorithms and show that D2D throughput improves drastically without undermining CUEs throughput

Accessibility of Website for Visually Challenged: Combined Tree Structure and XML Metadata

This paper presents two different approaches to provide an efficient multimedia interface for the visually challenged people. The first approach is the tree structured web contents, which integrates many different web pages into a single page. The second approach is the XML metadata for dynamically changing multimedia contents. This paper demonstrates how this combined approach of tree structure and metadata can help visually challenged users accessing multimedia data on the web

A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing

Genotoxicity Evaluation of Commercially Available Acid Red Dye by Comet Assay in Fish (Cyprinus Carpio)

Genotoxicity of commercially available acid red dye on fish was evaluated. Fish were exposed to various concentrations of the dye and gill processed for comet assay. Nucleoids were visually scored and categorized into various damage degrees. Significant increase (p < 0.05) in the percentage and distribution of damaged nucleoids was recorded in all dye-treated groups over control. DNA damage scores (AU) increased with exposure concentrations and dose-response was observed at higher doses. From the results it is concluded that commercially available acid red dye is potentially genotoxic to fish. The results are preliminary and further studies are warranted to acknowledge this effect. Keywords: acid red, commercial dyes, genotoxicity, comet assay

Intrusion Detection Recording System with Biometric Lock

The spread of COVID-19 in the entire world has put humankind in danger. The assets of probably the biggest economies are worried because of the enormous infectivity and contagiousness of this illness. The ability of machine learning algorithms to predict the number of possible COVID-19 patients is generally seen as a potential challenge to mankind. The undermining components of COVID-19 were determined using four normal estimating models: Support Vector Machine (SVM), least total shrinkage, and determination administrator (LASSO), linear regression (LR). Any one of the models makes three types of predictions, such as the number of newly infected occurrences, the number of passings, and the rate of recoveries, but they cannot predict the exact result for the patients. To defeat the issue, the Proposed strategy utilizing exponential smoothing (ES) The number of cases of COVID-19 and the impact of COVID-19 preventive steps including certain social insulation and latch on infectious diseases was expected in the next 30 days to come

Optimal placement of Femto base stations in enterprise femtocell networks

Femto cells a.k.a. Low Power Nodes (LPNs) are deployed to improve indoor data rates as well as reduce traffic load on macro Base Stations (BSs) in 4G/LTE cellular networks. Indoor UEs getting high SNR (Signal-to-Noise Ratio) can experience good throughput, but SNR decreases at faster rate due to obstacles, present along the communication path. Hence, efficient placement of Femtos in enterprise buildings is crucial to attain desirable SNR for indoor users. We consider obstacles and shadowing effects by walls and include them in the system model. We develop a Linear Programming Problem (LPP) model by converting convex constraints into linear ones and solve it using GAMS tool, to place Femtos optimally inside the building. Our extensive experimentation proves the optimal placement of Femtos achieves 14.41% and 35.95% increase in SNR of indoor UEs over random and center placement strategies, respectively